{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f2eb95a1",
   "metadata": {},
   "source": [
    "# SENet\n",
    "\n",
    "### 背景\n",
    "- **提出时间**：2017年由Momenta团队提出，发表于CVPR 2018，并获得 **ImageNet 2017分类竞赛冠军**。\n",
    "- **核心问题**：传统CNN的卷积操作平等对待所有通道（channel），但不同通道的特征重要性实际存在差异。例如，某些通道可能编码背景噪声，而另一些编码关键语义特征。\n",
    "\n",
    "### ImageNet 结果\n",
    "- **Top-5错误率**：SENet-154（基于ResNeXt）达到 **2.251%**，超越同期其他模型（如DPN、NASNet）。\n",
    "- **轻量版性能**：SE-ResNet-50仅增加约2%的计算量，但错误率相对下降10%以上。\n",
    "\n",
    "### 解决的问题\n",
    "- **通道间依赖关系建模**：通过动态学习通道权重，增强重要特征通道，抑制冗余通道。\n",
    "- **计算效率**：在几乎不增加计算成本的前提下提升模型性能。\n",
    "\n",
    "---\n",
    "\n",
    "### 创新性\n",
    "\n",
    "1. **Squeeze-and-Excitation（SE）模块**：\n",
    "   - **Squeeze**：全局平均池化（GAP）压缩空间维度（H×W→1×1），生成通道描述符。\n",
    "   - **Excitation**：通过全连接层（含ReLU和Sigmoid）学习通道间非线性关系，输出各通道的权重（0~1）。\n",
    "   - **Scale**：将权重与原特征图逐通道相乘，实现特征重校准。\n",
    "\n",
    "![alt text](resources/senet_arch.png \"Title\")\n",
    "\n",
    "\n",
    "2. **即插即用设计**：\n",
    "   SE模块可嵌入任何现有CNN架构（如ResNet、Inception、MobileNet），仅需少量代码修改。\n",
    "\n",
    "\n",
    "![alt text](resources/senet_detail.png \"Title\")\n",
    "\n",
    "\n",
    "### 对当代CNN的影响\n",
    "\n",
    "1. **注意力机制的普及**：\n",
    "   - SENet是首个将 **通道注意力（Channel Attention）** 引入主流CNN的工作，启发了后续的CBAM（空间+通道注意力）、ECA-Net（高效通道注意力）等。\n",
    "   - 推动了\"注意力即基础模块\"的范式转变（如Transformer中的Self-Attention）。\n",
    "\n",
    "2. **轻量化设计的标杆**：\n",
    "   - 证明通过智能特征重校准（而非单纯增加深度/宽度）可显著提升模型效率，影响MobileNetV3、EfficientNet等轻量模型的设计。\n",
    "\n",
    "3. **跨领域应用**：\n",
    "   - 医学图像（如病灶区域增强）、目标检测（如SE-FasterRCNN）、视频分析等均验证了SE模块的泛用性。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c82ed936",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8c0efce3",
   "metadata": {},
   "source": [
    "## SE模块实现\n",
    "SE模块和使用都非常简单,即插即用.\n",
    "```python\n",
    "class SqueezeExcitation(nn.Module):\n",
    "    \"\"\"Pluggable Squeeze-Excitation Module.\"\"\"\n",
    "\n",
    "    def __init__(self, channel: int, reduction_ratio: int) -> None:\n",
    "        \"\"\"Init.\n",
    "\n",
    "        Args:\n",
    "            channel: Number of channels.\n",
    "            reduction_ratio: Reduction ratio for the internal FC layer.\n",
    "        \"\"\"\n",
    "        fc_channel = channel // reduction_ratio\n",
    "        if fc_channel * reduction_ratio != channel:\n",
    "            raise ValueError(\"Channel is not divisible by reduction ratio.\")\n",
    "        self.se = nn.Sequential(\n",
    "            nn.AdaptiveAvgPool2d((1, 1)),\n",
    "            # 使用1x1卷积来模拟Linear\n",
    "            nn.Conv2d(channel, fc_channel, kernel_size=1, bias=False),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(fc_channel, channel, kernel_size=1, bias=False),\n",
    "            nn.Sigmoid(),\n",
    "        )\n",
    "\n",
    "    def forward(self, X: torch.Tensor) -> torch.Tensor:\n",
    "        return X * self.se(X)\n",
    "```\n",
    "\n",
    "一行代码即将其应用到Resnet的Bottleneck模块上:\n",
    "```python\n",
    "\n",
    "class SEBottleneckBlock(nn.Module):\n",
    "    def __init__(...) -> None:\n",
    "        ...\n",
    "        self.resbranch = nn.Sequential(\n",
    "            _Conv2d(in_channel, bottleneck_channel, activation=True, kernel_size=1, padding=0),\n",
    "            _Conv2d(bottleneck_channel, bottleneck_channel, activation=True, kernel_size=3, stride=stride, padding=1),\n",
    "            _Conv2d(bottleneck_channel, out_channel, activation=False, kernel_size=1, padding=0),\n",
    "            # 这个SqueezeExcitation模块是唯一和BottleneckBlock不同的地方\n",
    "            SqueezeExcitation(out_channel, reduction_ratio),\n",
    "        )\n",
    "    ...\n",
    "```\n",
    "\n",
    "下面对比Resnet50和SE-Resnet50两个网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c8b5588a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use device:  cuda\n"
     ]
    }
   ],
   "source": [
    "# 自动重新加载外部module，使得修改代码之后无需重新import\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from hdd.device.utils import get_device\n",
    "from hdd.dataset.imagenette_in_memory import ImagenetteInMemory\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# 设置训练数据的路径\n",
    "DATA_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置TensorBoard的路径\n",
    "TENSORBOARD_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置预训练模型参数路径\n",
    "TORCH_HUB_PATH = \"~/workspace/hands-dirty-on-dl/pretrained_models\"\n",
    "torch.hub.set_dir(TORCH_HUB_PATH)\n",
    "# 挑选最合适的训练设备\n",
    "DEVICE = get_device([\"cuda\", \"cpu\"])\n",
    "print(\"Use device: \", DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c86b49c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hdd.data_util.transforms import RandomResize\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "TRAIN_MEAN = [0.4625, 0.4580, 0.4295]\n",
    "TRAIN_STD = [0.2452, 0.2390, 0.2469]\n",
    "train_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        RandomResize([256, 296, 384]),  # 随机在三个size中选择一个进行resize\n",
    "        transforms.RandomRotation(10),\n",
    "        transforms.RandomCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "val_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "train_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"train\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=train_dataset_transforms,\n",
    ")\n",
    "val_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"val\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=val_dataset_transforms,\n",
    ")\n",
    "\n",
    "\n",
    "def build_dataloader(batch_size, train_dataset, val_dataset):\n",
    "    train_dataloader = DataLoader(\n",
    "        train_dataset, batch_size=batch_size, shuffle=True, num_workers=8\n",
    "    )\n",
    "    val_dataloader = DataLoader(\n",
    "        val_dataset, batch_size=batch_size, shuffle=False, num_workers=8\n",
    "    )\n",
    "    return train_dataloader, val_dataloader"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "390c69fd",
   "metadata": {},
   "source": [
    "### Resnet50"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "820be33a",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d54747f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/160 Train Loss: 2.4258 Accuracy: 0.2116 Time: 16.74283  | Val Loss: 2.0407 Accuracy: 0.2764\n",
      "Epoch: 2/160 Train Loss: 1.8985 Accuracy: 0.3391 Time: 15.66259  | Val Loss: 1.7327 Accuracy: 0.4270\n",
      "Epoch: 3/160 Train Loss: 1.6758 Accuracy: 0.4298 Time: 16.18152  | Val Loss: 1.7336 Accuracy: 0.4354\n",
      "Epoch: 4/160 Train Loss: 1.5121 Accuracy: 0.4934 Time: 16.27642  | Val Loss: 1.3863 Accuracy: 0.5445\n",
      "Epoch: 5/160 Train Loss: 1.3883 Accuracy: 0.5411 Time: 16.02157  | Val Loss: 1.3551 Accuracy: 0.5615\n",
      "Epoch: 6/160 Train Loss: 1.2680 Accuracy: 0.5869 Time: 16.21739  | Val Loss: 1.3528 Accuracy: 0.5944\n",
      "Epoch: 7/160 Train Loss: 1.1834 Accuracy: 0.6164 Time: 16.08554  | Val Loss: 1.3408 Accuracy: 0.5549\n",
      "Epoch: 8/160 Train Loss: 1.1112 Accuracy: 0.6421 Time: 16.65766  | Val Loss: 1.1828 Accuracy: 0.6359\n",
      "Epoch: 9/160 Train Loss: 1.0445 Accuracy: 0.6625 Time: 16.24557  | Val Loss: 0.8663 Accuracy: 0.7231\n",
      "Epoch: 10/160 Train Loss: 1.0128 Accuracy: 0.6769 Time: 16.25455  | Val Loss: 1.0816 Accuracy: 0.6545\n",
      "Epoch: 11/160 Train Loss: 0.9460 Accuracy: 0.7008 Time: 16.36087  | Val Loss: 1.4406 Accuracy: 0.5939\n",
      "Epoch: 12/160 Train Loss: 0.9077 Accuracy: 0.7122 Time: 16.20319  | Val Loss: 0.8677 Accuracy: 0.7192\n",
      "Epoch: 13/160 Train Loss: 0.8521 Accuracy: 0.7257 Time: 16.11278  | Val Loss: 1.0477 Accuracy: 0.6708\n",
      "Epoch: 14/160 Train Loss: 0.8468 Accuracy: 0.7270 Time: 15.81547  | Val Loss: 0.9423 Accuracy: 0.6866\n",
      "Epoch: 15/160 Train Loss: 0.8171 Accuracy: 0.7372 Time: 15.82797  | Val Loss: 0.7998 Accuracy: 0.7424\n",
      "Epoch: 16/160 Train Loss: 0.7692 Accuracy: 0.7521 Time: 16.21203  | Val Loss: 0.7106 Accuracy: 0.7776\n",
      "Epoch: 17/160 Train Loss: 0.7626 Accuracy: 0.7575 Time: 16.51377  | Val Loss: 0.8778 Accuracy: 0.7200\n",
      "Epoch: 18/160 Train Loss: 0.7552 Accuracy: 0.7587 Time: 16.14535  | Val Loss: 0.6833 Accuracy: 0.7783\n",
      "Epoch: 19/160 Train Loss: 0.7298 Accuracy: 0.7663 Time: 16.18129  | Val Loss: 0.8711 Accuracy: 0.7261\n",
      "Epoch: 20/160 Train Loss: 0.7072 Accuracy: 0.7705 Time: 15.93520  | Val Loss: 0.7529 Accuracy: 0.7704\n",
      "Epoch: 21/160 Train Loss: 0.6683 Accuracy: 0.7855 Time: 16.21936  | Val Loss: 0.6838 Accuracy: 0.7921\n",
      "Epoch: 22/160 Train Loss: 0.6672 Accuracy: 0.7836 Time: 16.44855  | Val Loss: 1.0560 Accuracy: 0.6915\n",
      "Epoch: 23/160 Train Loss: 0.6502 Accuracy: 0.7896 Time: 16.27225  | Val Loss: 0.9484 Accuracy: 0.7399\n",
      "Epoch: 24/160 Train Loss: 0.6351 Accuracy: 0.7980 Time: 16.38582  | Val Loss: 0.7001 Accuracy: 0.7829\n",
      "Epoch: 25/160 Train Loss: 0.6421 Accuracy: 0.7969 Time: 16.49683  | Val Loss: 0.8574 Accuracy: 0.7315\n",
      "Epoch: 26/160 Train Loss: 0.6173 Accuracy: 0.8041 Time: 16.24821  | Val Loss: 0.7219 Accuracy: 0.7822\n",
      "Epoch: 27/160 Train Loss: 0.5866 Accuracy: 0.8122 Time: 16.40996  | Val Loss: 0.5968 Accuracy: 0.8247\n",
      "Epoch: 28/160 Train Loss: 0.5839 Accuracy: 0.8102 Time: 16.18877  | Val Loss: 0.5499 Accuracy: 0.8206\n",
      "Epoch: 29/160 Train Loss: 0.5587 Accuracy: 0.8195 Time: 16.26702  | Val Loss: 0.6041 Accuracy: 0.8150\n",
      "Epoch: 30/160 Train Loss: 0.5559 Accuracy: 0.8215 Time: 16.19234  | Val Loss: 0.5985 Accuracy: 0.8043\n",
      "Epoch: 31/160 Train Loss: 0.5492 Accuracy: 0.8281 Time: 16.38251  | Val Loss: 0.6525 Accuracy: 0.7967\n",
      "Epoch: 32/160 Train Loss: 0.5428 Accuracy: 0.8272 Time: 15.95041  | Val Loss: 0.6673 Accuracy: 0.7969\n",
      "Epoch: 33/160 Train Loss: 0.5287 Accuracy: 0.8285 Time: 16.27911  | Val Loss: 0.6160 Accuracy: 0.8155\n",
      "Epoch: 34/160 Train Loss: 0.5260 Accuracy: 0.8297 Time: 16.20660  | Val Loss: 0.5742 Accuracy: 0.8227\n",
      "Epoch: 35/160 Train Loss: 0.5305 Accuracy: 0.8244 Time: 16.27051  | Val Loss: 0.7231 Accuracy: 0.7702\n",
      "Epoch: 36/160 Train Loss: 0.5222 Accuracy: 0.8357 Time: 16.24918  | Val Loss: 0.6813 Accuracy: 0.7926\n",
      "Epoch: 37/160 Train Loss: 0.5025 Accuracy: 0.8426 Time: 16.07583  | Val Loss: 0.5774 Accuracy: 0.8237\n",
      "Epoch: 38/160 Train Loss: 0.4893 Accuracy: 0.8402 Time: 15.45215  | Val Loss: 0.5489 Accuracy: 0.8255\n",
      "Epoch: 39/160 Train Loss: 0.4752 Accuracy: 0.8454 Time: 15.82429  | Val Loss: 0.4833 Accuracy: 0.8515\n",
      "Epoch: 40/160 Train Loss: 0.4694 Accuracy: 0.8480 Time: 15.82551  | Val Loss: 0.5978 Accuracy: 0.8161\n",
      "Epoch: 41/160 Train Loss: 0.4077 Accuracy: 0.8682 Time: 16.07382  | Val Loss: 0.3760 Accuracy: 0.8795\n",
      "Epoch: 42/160 Train Loss: 0.3458 Accuracy: 0.8893 Time: 15.69044  | Val Loss: 0.3711 Accuracy: 0.8823\n",
      "Epoch: 43/160 Train Loss: 0.3310 Accuracy: 0.8916 Time: 15.70410  | Val Loss: 0.3636 Accuracy: 0.8854\n",
      "Epoch: 44/160 Train Loss: 0.3267 Accuracy: 0.8980 Time: 16.02655  | Val Loss: 0.3624 Accuracy: 0.8846\n",
      "Epoch: 45/160 Train Loss: 0.3139 Accuracy: 0.8970 Time: 15.95328  | Val Loss: 0.3637 Accuracy: 0.8864\n",
      "Epoch: 46/160 Train Loss: 0.3163 Accuracy: 0.8962 Time: 15.78963  | Val Loss: 0.3534 Accuracy: 0.8897\n",
      "Epoch: 47/160 Train Loss: 0.2971 Accuracy: 0.9039 Time: 15.83740  | Val Loss: 0.3497 Accuracy: 0.8882\n",
      "Epoch: 48/160 Train Loss: 0.3034 Accuracy: 0.9033 Time: 15.93107  | Val Loss: 0.3618 Accuracy: 0.8887\n",
      "Epoch: 49/160 Train Loss: 0.3046 Accuracy: 0.8997 Time: 15.73241  | Val Loss: 0.3596 Accuracy: 0.8864\n",
      "Epoch: 50/160 Train Loss: 0.2909 Accuracy: 0.9066 Time: 15.68554  | Val Loss: 0.3519 Accuracy: 0.8882\n",
      "Epoch: 51/160 Train Loss: 0.2830 Accuracy: 0.9085 Time: 15.76542  | Val Loss: 0.3686 Accuracy: 0.8833\n",
      "Epoch: 52/160 Train Loss: 0.2905 Accuracy: 0.9055 Time: 15.64620  | Val Loss: 0.3532 Accuracy: 0.8922\n",
      "Epoch: 53/160 Train Loss: 0.2909 Accuracy: 0.9066 Time: 16.18119  | Val Loss: 0.3490 Accuracy: 0.8927\n",
      "Epoch: 54/160 Train Loss: 0.2812 Accuracy: 0.9080 Time: 15.66303  | Val Loss: 0.3547 Accuracy: 0.8899\n",
      "Epoch: 55/160 Train Loss: 0.2830 Accuracy: 0.9111 Time: 15.73684  | Val Loss: 0.3573 Accuracy: 0.8904\n",
      "Epoch: 56/160 Train Loss: 0.2656 Accuracy: 0.9121 Time: 15.65313  | Val Loss: 0.3517 Accuracy: 0.8938\n",
      "Epoch: 57/160 Train Loss: 0.2679 Accuracy: 0.9101 Time: 15.79363  | Val Loss: 0.3539 Accuracy: 0.8935\n",
      "Epoch: 58/160 Train Loss: 0.2721 Accuracy: 0.9123 Time: 15.68871  | Val Loss: 0.3490 Accuracy: 0.8966\n",
      "Epoch: 59/160 Train Loss: 0.2550 Accuracy: 0.9199 Time: 16.12675  | Val Loss: 0.3501 Accuracy: 0.8961\n",
      "Epoch: 60/160 Train Loss: 0.2727 Accuracy: 0.9093 Time: 15.58849  | Val Loss: 0.3574 Accuracy: 0.8917\n",
      "Epoch: 61/160 Train Loss: 0.2569 Accuracy: 0.9188 Time: 15.89483  | Val Loss: 0.3527 Accuracy: 0.8907\n",
      "Epoch: 62/160 Train Loss: 0.2542 Accuracy: 0.9184 Time: 16.06166  | Val Loss: 0.3479 Accuracy: 0.8938\n",
      "Epoch: 63/160 Train Loss: 0.2451 Accuracy: 0.9193 Time: 15.94611  | Val Loss: 0.3627 Accuracy: 0.8938\n",
      "Epoch: 64/160 Train Loss: 0.2514 Accuracy: 0.9199 Time: 15.79370  | Val Loss: 0.3520 Accuracy: 0.8948\n",
      "Epoch: 65/160 Train Loss: 0.2556 Accuracy: 0.9156 Time: 15.63803  | Val Loss: 0.3434 Accuracy: 0.8973\n",
      "Epoch: 66/160 Train Loss: 0.2424 Accuracy: 0.9185 Time: 15.48522  | Val Loss: 0.3627 Accuracy: 0.8925\n",
      "Epoch: 67/160 Train Loss: 0.2492 Accuracy: 0.9175 Time: 15.51072  | Val Loss: 0.3460 Accuracy: 0.8950\n",
      "Epoch: 68/160 Train Loss: 0.2351 Accuracy: 0.9246 Time: 15.84131  | Val Loss: 0.3660 Accuracy: 0.8882\n",
      "Epoch: 69/160 Train Loss: 0.2328 Accuracy: 0.9245 Time: 15.85460  | Val Loss: 0.3526 Accuracy: 0.8925\n",
      "Epoch: 70/160 Train Loss: 0.2474 Accuracy: 0.9146 Time: 15.89946  | Val Loss: 0.3462 Accuracy: 0.8917\n",
      "Epoch: 71/160 Train Loss: 0.2320 Accuracy: 0.9230 Time: 15.79886  | Val Loss: 0.3573 Accuracy: 0.8871\n",
      "Epoch: 72/160 Train Loss: 0.2288 Accuracy: 0.9247 Time: 15.92567  | Val Loss: 0.3542 Accuracy: 0.8907\n",
      "Epoch: 73/160 Train Loss: 0.2332 Accuracy: 0.9244 Time: 15.66409  | Val Loss: 0.3530 Accuracy: 0.8953\n",
      "Epoch: 74/160 Train Loss: 0.2131 Accuracy: 0.9316 Time: 15.72720  | Val Loss: 0.3568 Accuracy: 0.8907\n",
      "Epoch: 75/160 Train Loss: 0.2364 Accuracy: 0.9244 Time: 15.63829  | Val Loss: 0.3667 Accuracy: 0.8887\n",
      "Epoch: 76/160 Train Loss: 0.2266 Accuracy: 0.9267 Time: 15.66962  | Val Loss: 0.3605 Accuracy: 0.8945\n",
      "Epoch: 77/160 Train Loss: 0.2203 Accuracy: 0.9258 Time: 15.77867  | Val Loss: 0.3569 Accuracy: 0.8971\n",
      "Epoch: 78/160 Train Loss: 0.2250 Accuracy: 0.9288 Time: 15.77854  | Val Loss: 0.3640 Accuracy: 0.8986\n",
      "Epoch: 79/160 Train Loss: 0.2173 Accuracy: 0.9274 Time: 15.93026  | Val Loss: 0.3592 Accuracy: 0.8948\n",
      "Epoch: 80/160 Train Loss: 0.2306 Accuracy: 0.9236 Time: 15.83328  | Val Loss: 0.3694 Accuracy: 0.8897\n",
      "Epoch: 81/160 Train Loss: 0.2081 Accuracy: 0.9327 Time: 15.73244  | Val Loss: 0.3574 Accuracy: 0.8935\n",
      "Epoch: 82/160 Train Loss: 0.2018 Accuracy: 0.9346 Time: 15.67360  | Val Loss: 0.3524 Accuracy: 0.8940\n",
      "Epoch: 83/160 Train Loss: 0.2082 Accuracy: 0.9326 Time: 15.70641  | Val Loss: 0.3564 Accuracy: 0.8932\n",
      "Epoch: 84/160 Train Loss: 0.2068 Accuracy: 0.9310 Time: 15.46079  | Val Loss: 0.3487 Accuracy: 0.8966\n",
      "Epoch: 85/160 Train Loss: 0.2038 Accuracy: 0.9334 Time: 15.66337  | Val Loss: 0.3487 Accuracy: 0.8950\n",
      "Epoch: 86/160 Train Loss: 0.2015 Accuracy: 0.9326 Time: 15.54653  | Val Loss: 0.3501 Accuracy: 0.8958\n",
      "Epoch: 87/160 Train Loss: 0.1999 Accuracy: 0.9336 Time: 15.50800  | Val Loss: 0.3489 Accuracy: 0.8950\n",
      "Epoch: 88/160 Train Loss: 0.1999 Accuracy: 0.9353 Time: 15.52265  | Val Loss: 0.3492 Accuracy: 0.8958\n",
      "Epoch: 89/160 Train Loss: 0.1932 Accuracy: 0.9358 Time: 15.61155  | Val Loss: 0.3470 Accuracy: 0.8971\n",
      "Epoch: 90/160 Train Loss: 0.2015 Accuracy: 0.9371 Time: 15.58207  | Val Loss: 0.3470 Accuracy: 0.8958\n",
      "Epoch: 91/160 Train Loss: 0.1972 Accuracy: 0.9339 Time: 15.61691  | Val Loss: 0.3451 Accuracy: 0.8991\n",
      "Epoch: 92/160 Train Loss: 0.2034 Accuracy: 0.9339 Time: 15.55909  | Val Loss: 0.3440 Accuracy: 0.8966\n",
      "Epoch: 93/160 Train Loss: 0.1990 Accuracy: 0.9358 Time: 15.51532  | Val Loss: 0.3466 Accuracy: 0.8978\n",
      "Epoch: 94/160 Train Loss: 0.2045 Accuracy: 0.9305 Time: 15.81138  | Val Loss: 0.3475 Accuracy: 0.8976\n",
      "Epoch: 95/160 Train Loss: 0.1968 Accuracy: 0.9373 Time: 15.89185  | Val Loss: 0.3492 Accuracy: 0.8950\n",
      "Epoch: 96/160 Train Loss: 0.2046 Accuracy: 0.9358 Time: 16.23152  | Val Loss: 0.3502 Accuracy: 0.8966\n",
      "Epoch: 97/160 Train Loss: 0.2023 Accuracy: 0.9348 Time: 15.72050  | Val Loss: 0.3467 Accuracy: 0.8983\n",
      "Epoch: 98/160 Train Loss: 0.1935 Accuracy: 0.9377 Time: 15.81431  | Val Loss: 0.3478 Accuracy: 0.8971\n",
      "Epoch: 99/160 Train Loss: 0.1890 Accuracy: 0.9398 Time: 15.85293  | Val Loss: 0.3451 Accuracy: 0.8983\n",
      "Epoch: 100/160 Train Loss: 0.1927 Accuracy: 0.9378 Time: 15.83791  | Val Loss: 0.3524 Accuracy: 0.8963\n",
      "Epoch: 101/160 Train Loss: 0.1953 Accuracy: 0.9401 Time: 16.04491  | Val Loss: 0.3507 Accuracy: 0.8968\n",
      "Epoch: 102/160 Train Loss: 0.2017 Accuracy: 0.9339 Time: 15.85862  | Val Loss: 0.3516 Accuracy: 0.8981\n",
      "Epoch: 103/160 Train Loss: 0.1997 Accuracy: 0.9351 Time: 15.88870  | Val Loss: 0.3542 Accuracy: 0.8966\n",
      "Epoch: 104/160 Train Loss: 0.1965 Accuracy: 0.9357 Time: 15.68531  | Val Loss: 0.3464 Accuracy: 0.8991\n",
      "Epoch: 105/160 Train Loss: 0.1871 Accuracy: 0.9394 Time: 15.95406  | Val Loss: 0.3438 Accuracy: 0.8978\n",
      "Early stop at epoch 105!\n",
      "#Parameter: 23528522 Accuracy: 0.8973248407643312\n"
     ]
    }
   ],
   "source": [
    "from spacy import training\n",
    "from hdd.models.cnn.resnet import Resnet, resnet50_config\n",
    "from hdd.train.early_stopping import EarlyStoppingInMem\n",
    "from hdd.train.classification_utils import (\n",
    "    naive_train_classification_model,\n",
    "    eval_image_classifier,\n",
    ")\n",
    "from hdd.models.nn_utils import count_trainable_parameter\n",
    "\n",
    "\n",
    "def train_net(\n",
    "    resnet_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout,\n",
    "    lr,\n",
    "    weight_decay,\n",
    "    step_size=40,\n",
    "    gamma=0.1,\n",
    "    patience=40,\n",
    "    max_epochs=160,\n",
    ") -> tuple[Resnet, dict[str, list[float]]]:\n",
    "    net = Resnet(resnet_config, num_classes=10, dropout=dropout).to(DEVICE)\n",
    "    criteria = nn.CrossEntropyLoss()\n",
    "    # SGD的效果远不如Adam好\n",
    "    # optimizer = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9)\n",
    "    optimizer = optim.AdamW(\n",
    "        net.parameters(), lr=lr, eps=1e-6, weight_decay=weight_decay\n",
    "    )\n",
    "    scheduler = torch.optim.lr_scheduler.StepLR(\n",
    "        optimizer, step_size=step_size, gamma=gamma, last_epoch=-1\n",
    "    )\n",
    "    early_stopper = EarlyStoppingInMem(patience=patience, verbose=False)\n",
    "    training_stats = naive_train_classification_model(\n",
    "        net,\n",
    "        criteria,\n",
    "        max_epochs,\n",
    "        train_dataloader,\n",
    "        val_dataloader,\n",
    "        DEVICE,\n",
    "        optimizer,\n",
    "        scheduler,\n",
    "        early_stopper,\n",
    "        verbose=True,\n",
    "    )\n",
    "    return net, training_stats\n",
    "\n",
    "\n",
    "train_dataloader, val_dataloader = build_dataloader(128, train_dataset, val_dataset)\n",
    "net, resnet50_stats = train_net(\n",
    "    resnet50_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout=0.5,\n",
    "    lr=0.01,\n",
    "    weight_decay=1e-4,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "621c7a2e",
   "metadata": {},
   "source": [
    "### SEResnet-50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59708565",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/160 Train Loss: 1.9854 Accuracy: 0.3185 Time: 19.05724  | Val Loss: 1.6836 Accuracy: 0.4273\n",
      "Epoch: 2/160 Train Loss: 1.6439 Accuracy: 0.4401 Time: 19.38730  | Val Loss: 1.5655 Accuracy: 0.4650\n",
      "Epoch: 3/160 Train Loss: 1.4149 Accuracy: 0.5304 Time: 19.25748  | Val Loss: 1.8216 Accuracy: 0.4479\n",
      "Epoch: 4/160 Train Loss: 1.2618 Accuracy: 0.5838 Time: 19.47411  | Val Loss: 1.1348 Accuracy: 0.6326\n",
      "Epoch: 5/160 Train Loss: 1.1586 Accuracy: 0.6194 Time: 19.44161  | Val Loss: 1.0522 Accuracy: 0.6670\n",
      "Epoch: 6/160 Train Loss: 1.0450 Accuracy: 0.6597 Time: 19.38560  | Val Loss: 1.1841 Accuracy: 0.6094\n",
      "Epoch: 7/160 Train Loss: 1.0015 Accuracy: 0.6773 Time: 19.46964  | Val Loss: 1.0668 Accuracy: 0.6596\n",
      "Epoch: 8/160 Train Loss: 0.9163 Accuracy: 0.7018 Time: 19.24141  | Val Loss: 1.1453 Accuracy: 0.6346\n",
      "Epoch: 9/160 Train Loss: 0.8665 Accuracy: 0.7215 Time: 19.47773  | Val Loss: 0.9451 Accuracy: 0.7024\n",
      "Epoch: 10/160 Train Loss: 0.8273 Accuracy: 0.7328 Time: 19.42424  | Val Loss: 0.8375 Accuracy: 0.7266\n",
      "Epoch: 11/160 Train Loss: 0.7897 Accuracy: 0.7464 Time: 18.93511  | Val Loss: 0.7153 Accuracy: 0.7761\n",
      "Epoch: 12/160 Train Loss: 0.7478 Accuracy: 0.7624 Time: 19.39582  | Val Loss: 1.0007 Accuracy: 0.7073\n",
      "Epoch: 13/160 Train Loss: 0.7326 Accuracy: 0.7614 Time: 24.20434  | Val Loss: 0.8795 Accuracy: 0.7373\n",
      "Epoch: 14/160 Train Loss: 0.7198 Accuracy: 0.7698 Time: 31.20587  | Val Loss: 0.8554 Accuracy: 0.7307\n",
      "Epoch: 15/160 Train Loss: 0.6952 Accuracy: 0.7756 Time: 31.41219  | Val Loss: 0.7764 Accuracy: 0.7666\n",
      "Epoch: 16/160 Train Loss: 0.6668 Accuracy: 0.7841 Time: 29.33893  | Val Loss: 0.7497 Accuracy: 0.7552\n",
      "Epoch: 17/160 Train Loss: 0.6593 Accuracy: 0.7879 Time: 19.38782  | Val Loss: 1.0579 Accuracy: 0.6795\n",
      "Epoch: 18/160 Train Loss: 0.6305 Accuracy: 0.7964 Time: 18.64714  | Val Loss: 0.7034 Accuracy: 0.7740\n",
      "Epoch: 19/160 Train Loss: 0.6111 Accuracy: 0.8002 Time: 19.35818  | Val Loss: 0.7146 Accuracy: 0.7822\n",
      "Epoch: 20/160 Train Loss: 0.6050 Accuracy: 0.8047 Time: 19.27269  | Val Loss: 0.7095 Accuracy: 0.7855\n",
      "Epoch: 21/160 Train Loss: 0.5773 Accuracy: 0.8104 Time: 19.53320  | Val Loss: 0.6216 Accuracy: 0.8074\n",
      "Epoch: 22/160 Train Loss: 0.5683 Accuracy: 0.8148 Time: 19.48051  | Val Loss: 0.6902 Accuracy: 0.7750\n",
      "Epoch: 23/160 Train Loss: 0.5648 Accuracy: 0.8214 Time: 19.47710  | Val Loss: 0.6450 Accuracy: 0.7990\n",
      "Epoch: 24/160 Train Loss: 0.5327 Accuracy: 0.8265 Time: 19.48504  | Val Loss: 0.6178 Accuracy: 0.8099\n",
      "Epoch: 25/160 Train Loss: 0.5289 Accuracy: 0.8235 Time: 19.50942  | Val Loss: 0.8274 Accuracy: 0.7460\n",
      "Epoch: 26/160 Train Loss: 0.5129 Accuracy: 0.8322 Time: 19.03535  | Val Loss: 0.6014 Accuracy: 0.8155\n",
      "Epoch: 27/160 Train Loss: 0.5208 Accuracy: 0.8325 Time: 19.34812  | Val Loss: 0.8578 Accuracy: 0.7546\n",
      "Epoch: 28/160 Train Loss: 0.5015 Accuracy: 0.8358 Time: 25.98763  | Val Loss: 0.5709 Accuracy: 0.8318\n",
      "Epoch: 29/160 Train Loss: 0.5210 Accuracy: 0.8324 Time: 31.44909  | Val Loss: 0.5822 Accuracy: 0.8155\n",
      "Epoch: 30/160 Train Loss: 0.4688 Accuracy: 0.8478 Time: 30.90835  | Val Loss: 0.7671 Accuracy: 0.7824\n",
      "Epoch: 31/160 Train Loss: 0.4740 Accuracy: 0.8469 Time: 31.10094  | Val Loss: 0.8850 Accuracy: 0.7503\n",
      "Epoch: 32/160 Train Loss: 0.4638 Accuracy: 0.8500 Time: 20.55298  | Val Loss: 0.5305 Accuracy: 0.8364\n",
      "Epoch: 33/160 Train Loss: 0.4585 Accuracy: 0.8574 Time: 19.47608  | Val Loss: 0.5751 Accuracy: 0.8191\n",
      "Epoch: 34/160 Train Loss: 0.4528 Accuracy: 0.8510 Time: 19.13664  | Val Loss: 0.5307 Accuracy: 0.8352\n",
      "Epoch: 35/160 Train Loss: 0.4420 Accuracy: 0.8576 Time: 18.97485  | Val Loss: 0.6127 Accuracy: 0.8104\n",
      "Epoch: 36/160 Train Loss: 0.4312 Accuracy: 0.8626 Time: 18.93305  | Val Loss: 0.5565 Accuracy: 0.8273\n",
      "Epoch: 37/160 Train Loss: 0.4315 Accuracy: 0.8632 Time: 18.77295  | Val Loss: 0.5310 Accuracy: 0.8339\n",
      "Epoch: 38/160 Train Loss: 0.4044 Accuracy: 0.8711 Time: 18.82195  | Val Loss: 0.5269 Accuracy: 0.8405\n",
      "Epoch: 39/160 Train Loss: 0.3987 Accuracy: 0.8677 Time: 19.05693  | Val Loss: 0.4697 Accuracy: 0.8525\n",
      "Epoch: 40/160 Train Loss: 0.3951 Accuracy: 0.8698 Time: 18.77723  | Val Loss: 0.5290 Accuracy: 0.8324\n",
      "Epoch: 41/160 Train Loss: 0.3217 Accuracy: 0.8986 Time: 18.98172  | Val Loss: 0.3839 Accuracy: 0.8843\n",
      "Epoch: 42/160 Train Loss: 0.2789 Accuracy: 0.9130 Time: 18.94552  | Val Loss: 0.3578 Accuracy: 0.8922\n",
      "Epoch: 43/160 Train Loss: 0.2756 Accuracy: 0.9115 Time: 18.98691  | Val Loss: 0.3627 Accuracy: 0.8897\n",
      "Epoch: 44/160 Train Loss: 0.2703 Accuracy: 0.9122 Time: 18.91099  | Val Loss: 0.3590 Accuracy: 0.8899\n",
      "Epoch: 45/160 Train Loss: 0.2593 Accuracy: 0.9166 Time: 18.62041  | Val Loss: 0.3571 Accuracy: 0.8917\n",
      "Epoch: 46/160 Train Loss: 0.2572 Accuracy: 0.9171 Time: 18.87591  | Val Loss: 0.3662 Accuracy: 0.8917\n",
      "Epoch: 47/160 Train Loss: 0.2537 Accuracy: 0.9196 Time: 18.86945  | Val Loss: 0.3528 Accuracy: 0.8955\n",
      "Epoch: 48/160 Train Loss: 0.2493 Accuracy: 0.9175 Time: 19.32074  | Val Loss: 0.3532 Accuracy: 0.8945\n",
      "Epoch: 49/160 Train Loss: 0.2443 Accuracy: 0.9207 Time: 19.47812  | Val Loss: 0.3512 Accuracy: 0.8945\n",
      "Epoch: 50/160 Train Loss: 0.2356 Accuracy: 0.9235 Time: 19.27698  | Val Loss: 0.3547 Accuracy: 0.8925\n",
      "Epoch: 51/160 Train Loss: 0.2418 Accuracy: 0.9226 Time: 19.42570  | Val Loss: 0.3444 Accuracy: 0.8976\n",
      "Epoch: 52/160 Train Loss: 0.2263 Accuracy: 0.9266 Time: 19.41186  | Val Loss: 0.3477 Accuracy: 0.8991\n",
      "Epoch: 53/160 Train Loss: 0.2263 Accuracy: 0.9236 Time: 19.41855  | Val Loss: 0.3527 Accuracy: 0.8917\n",
      "Epoch: 54/160 Train Loss: 0.2287 Accuracy: 0.9243 Time: 19.50755  | Val Loss: 0.3553 Accuracy: 0.8958\n",
      "Epoch: 55/160 Train Loss: 0.2172 Accuracy: 0.9281 Time: 19.54540  | Val Loss: 0.3514 Accuracy: 0.8973\n",
      "Epoch: 56/160 Train Loss: 0.2156 Accuracy: 0.9296 Time: 19.55072  | Val Loss: 0.3583 Accuracy: 0.8938\n",
      "Epoch: 57/160 Train Loss: 0.2184 Accuracy: 0.9285 Time: 19.40934  | Val Loss: 0.3473 Accuracy: 0.8976\n",
      "Epoch: 58/160 Train Loss: 0.2074 Accuracy: 0.9319 Time: 19.48632  | Val Loss: 0.3592 Accuracy: 0.8955\n",
      "Epoch: 59/160 Train Loss: 0.2108 Accuracy: 0.9320 Time: 19.53028  | Val Loss: 0.3563 Accuracy: 0.8922\n",
      "Epoch: 60/160 Train Loss: 0.2101 Accuracy: 0.9320 Time: 19.37065  | Val Loss: 0.3562 Accuracy: 0.8938\n",
      "Epoch: 61/160 Train Loss: 0.2082 Accuracy: 0.9317 Time: 19.44679  | Val Loss: 0.3635 Accuracy: 0.8935\n",
      "Epoch: 62/160 Train Loss: 0.1993 Accuracy: 0.9337 Time: 19.43705  | Val Loss: 0.3666 Accuracy: 0.8943\n",
      "Epoch: 63/160 Train Loss: 0.2058 Accuracy: 0.9319 Time: 18.79911  | Val Loss: 0.3631 Accuracy: 0.8938\n",
      "Epoch: 64/160 Train Loss: 0.1994 Accuracy: 0.9356 Time: 18.52670  | Val Loss: 0.3667 Accuracy: 0.8948\n",
      "Epoch: 65/160 Train Loss: 0.1972 Accuracy: 0.9370 Time: 18.80782  | Val Loss: 0.3769 Accuracy: 0.8907\n",
      "Epoch: 66/160 Train Loss: 0.1974 Accuracy: 0.9364 Time: 18.85747  | Val Loss: 0.3650 Accuracy: 0.8945\n",
      "Epoch: 67/160 Train Loss: 0.1944 Accuracy: 0.9373 Time: 18.83669  | Val Loss: 0.3661 Accuracy: 0.8945\n",
      "Epoch: 68/160 Train Loss: 0.1928 Accuracy: 0.9360 Time: 18.74585  | Val Loss: 0.3509 Accuracy: 0.8983\n",
      "Epoch: 69/160 Train Loss: 0.1906 Accuracy: 0.9367 Time: 18.55423  | Val Loss: 0.3567 Accuracy: 0.8955\n",
      "Epoch: 70/160 Train Loss: 0.1880 Accuracy: 0.9399 Time: 18.49043  | Val Loss: 0.3630 Accuracy: 0.8976\n",
      "Epoch: 71/160 Train Loss: 0.1923 Accuracy: 0.9401 Time: 18.53549  | Val Loss: 0.3600 Accuracy: 0.8927\n",
      "Epoch: 72/160 Train Loss: 0.1798 Accuracy: 0.9405 Time: 18.86454  | Val Loss: 0.3549 Accuracy: 0.8983\n",
      "Epoch: 73/160 Train Loss: 0.1840 Accuracy: 0.9447 Time: 18.60003  | Val Loss: 0.3647 Accuracy: 0.8999\n",
      "Epoch: 74/160 Train Loss: 0.1848 Accuracy: 0.9401 Time: 18.70364  | Val Loss: 0.3664 Accuracy: 0.8976\n",
      "Epoch: 75/160 Train Loss: 0.1847 Accuracy: 0.9410 Time: 18.68507  | Val Loss: 0.3708 Accuracy: 0.8948\n",
      "Epoch: 76/160 Train Loss: 0.1785 Accuracy: 0.9397 Time: 18.92728  | Val Loss: 0.3564 Accuracy: 0.8981\n",
      "Epoch: 77/160 Train Loss: 0.1831 Accuracy: 0.9401 Time: 18.75448  | Val Loss: 0.3585 Accuracy: 0.8994\n",
      "Epoch: 78/160 Train Loss: 0.1780 Accuracy: 0.9428 Time: 18.46657  | Val Loss: 0.3680 Accuracy: 0.8966\n",
      "Epoch: 79/160 Train Loss: 0.1698 Accuracy: 0.9455 Time: 18.42250  | Val Loss: 0.3517 Accuracy: 0.9042\n",
      "Epoch: 80/160 Train Loss: 0.1735 Accuracy: 0.9445 Time: 18.42226  | Val Loss: 0.3673 Accuracy: 0.8945\n",
      "Epoch: 81/160 Train Loss: 0.1581 Accuracy: 0.9484 Time: 19.36918  | Val Loss: 0.3654 Accuracy: 0.8943\n",
      "Epoch: 82/160 Train Loss: 0.1712 Accuracy: 0.9448 Time: 19.52331  | Val Loss: 0.3591 Accuracy: 0.8971\n",
      "Epoch: 83/160 Train Loss: 0.1592 Accuracy: 0.9497 Time: 19.49139  | Val Loss: 0.3577 Accuracy: 0.8994\n",
      "Epoch: 84/160 Train Loss: 0.1636 Accuracy: 0.9464 Time: 19.59587  | Val Loss: 0.3579 Accuracy: 0.8996\n",
      "Epoch: 85/160 Train Loss: 0.1597 Accuracy: 0.9480 Time: 19.37609  | Val Loss: 0.3542 Accuracy: 0.8983\n",
      "Epoch: 86/160 Train Loss: 0.1516 Accuracy: 0.9507 Time: 19.43433  | Val Loss: 0.3537 Accuracy: 0.8994\n",
      "Epoch: 87/160 Train Loss: 0.1680 Accuracy: 0.9466 Time: 19.46688  | Val Loss: 0.3524 Accuracy: 0.8996\n",
      "Epoch: 88/160 Train Loss: 0.1534 Accuracy: 0.9507 Time: 19.46357  | Val Loss: 0.3511 Accuracy: 0.8999\n",
      "Epoch: 89/160 Train Loss: 0.1567 Accuracy: 0.9474 Time: 19.52710  | Val Loss: 0.3499 Accuracy: 0.9022\n",
      "Epoch: 90/160 Train Loss: 0.1593 Accuracy: 0.9495 Time: 19.44381  | Val Loss: 0.3495 Accuracy: 0.9009\n",
      "Epoch: 91/160 Train Loss: 0.1558 Accuracy: 0.9499 Time: 19.34866  | Val Loss: 0.3525 Accuracy: 0.9006\n",
      "Early stop at epoch 91!\n",
      "#Parameter: 26043466 Accuracy: 0.8975796178343949\n"
     ]
    }
   ],
   "source": [
    "from hdd.models.cnn.resnet import se_resnet50_config\n",
    "\n",
    "net, se_resnet50_stats = train_net(\n",
    "    se_resnet50_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout=0.5,\n",
    "    lr=0.005,\n",
    "    weight_decay=1e-4,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "12f4d313",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "fields = resnet50_stats.keys()\n",
    "for i, field in enumerate(fields):\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    plt.plot(resnet50_stats[field], label=\"Resnet50\", linestyle=\"--\")\n",
    "    plt.plot(se_resnet50_stats[field], label=\"SE_Resnet50\")\n",
    "    plt.legend()\n",
    "    plt.title(field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a0a9d6e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-cu124",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
